Predicting target characteristic data

ABSTRACT

Target characteristic data may be predicted using an apparatus including a processor and one or more computer readable mediums collectively including instructions. When executed by the processor, the instructions cause the processor to obtain a plurality of physical structure data and a plurality of characteristic data, estimate at least one structural similarity between at least two physical structures that correspond with physical structure data among the plurality of physical structure data, and generate an estimation model for estimating a target characteristic data from a target physical structure data by using at least one characteristic data and corresponding at least one structural similarity between the target physical structure data and each of the plurality of the physical structure data.

BACKGROUND Technical Field

The present invention relates to predicting target characteristic data.

Related Art

Computer aided engineering (CAE) has been utilized for a variety ofmanufacturing industries such as cars and electronic appliances. Forexample, design of car hoods is one of the applications of CAE. Strictregulations are being imposed on designs of car hoods so as to satisfysafety standards (e.g., injury scores on head-hood impacts). The injuryscores are computed from acceleration-time curves during head-hoodimpacts. The acceleration time-curves can be measured from expensivephysical crash tests, or estimated from relatively cheap computersimulations of crashes. However, it currently takes a long time toestimate an accelerating-time curve based on a computer simulation of acrash due to the large amount of memory and processor required tosimulate a crash.

SUMMARY

Therefore, it is an object of an aspect of the innovations herein topredict target characteristic data in a manner capable of overcoming theabove drawbacks accompanying the related art. The above and otherobjects can be achieved by combinations described in the claims. A firstaspect of the innovations may be an apparatus including a processor andone or more computer readable mediums collectively includinginstructions. When executed by the processor, the instructions may causethe processor to obtain a plurality of physical structure data and aplurality of characteristic data, wherein each physical structurecorresponds a characteristic data among the plurality of characteristicdata and characteristic data includes a plurality of characteristicvalues, each characteristic value being related to a physical structurethat corresponds with a physical structure data among the plurality ofphysical structure data, estimate at least one structural similaritybetween at least two physical structures that correspond with physicalstructure data among the plurality of physical structure data, andgenerate an estimation model for estimating a target characteristic datafrom a target physical structure data by using at least onecharacteristic data and corresponding at least one structural similaritybetween the target physical structure data and each of the plurality ofthe physical structure data. According to a first aspect of theinnovations, an apparatus may directly estimate characteristic data,such as an acceleration-time curve, from physical structure data,without simulating the crash.

The first aspect may also be a computer-implemented method that performsthe operations of the apparatus, or a computer program productcomprising a computer readable storage medium having programinstructions embodied therewith, the program instructions executable toperform the operations of the apparatus.

The summary clause does not necessarily describe all necessary featuresof the embodiments of the present invention. The present invention mayalso be a sub-combination of the features described above. The above andother features and advantages of the present invention will become moreapparent from the following description of the embodiments taken inconjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an application of target characteristic data prediction,according to an embodiment of the present invention.

FIG. 2 shows an acceleration-time curve estimated by predicting targetcharacteristic data, according to an embodiment of the presentinvention.

FIG. 3 shows an exemplary configuration of an apparatus 100, accordingto an embodiment of the present invention.

FIG. 4 shows an operational flow of an apparatus, according to anembodiment of the present invention.

FIG. 5 shows one example of physical structure data X_(i), according toan embodiment of the present invention.

FIG. 6 shows one example of characteristic data Y_(i), according to anembodiment of the present invention.

FIG. 7 shows one example of first learning, according to an embodimentof the present invention.

FIG. 8 shows one example of φ(X_(i)), according to an embodiment of thepresent invention.

FIG. 9 shows one example of V(X_(i), X_(n)), according to an embodimentof the present invention.

FIG. 10 shows one example of a response surface of an acceleration-timecurve, according to an embodiment of the present invention.

FIG. 11 shows a computer, according to an embodiment of the presentinvention.

DETAILED DESCRIPTION

Hereinafter, some embodiments of the present invention will bedescribed. The embodiments do not limit the invention according to theclaims, and the combinations of features described in the embodimentsare not necessarily essential to means provided by aspects of theinvention.

FIG. 1 shows an application of target characteristic data prediction,according to an embodiment of the present invention. A targetcharacteristic data predicting apparatus may generate an estimationmodel for estimating target characteristic data from target physicalstructure data of a physical structure. The estimation model may begenerated by utilizing training data of physical structures havingmeasured characteristic data. Each physical structure of the trainingdata may represent a part of a body of a mobile object, such as a carhood 12 of a car 10 shown in FIG. 1.

In the embodiment of FIG. 1, the car hood 12 has 20 points, a targetcharacteristic data predicting apparatus may input physical structuredata of these 20 points, and may output target characteristic data forthese 20 points. One of the 20 points is indicated as a point P_(i) inFIG. 1. In one embodiment, the apparatus may use physical structure dataXi of the point P_(i) and output characteristic data Y_(i) of the pointP_(i).

FIG. 2 shows an acceleration-time curve utilized for predicting targetcharacteristic data, according to an embodiment of the presentinvention. A target characteristic data predicting apparatus mayestimate an acceleration-time curve by utilizing training data, such asthe acceleration-time curve shown in FIG. 2. For example, the apparatusmay output the acceleration-time curves during a crash for the 20 pointsin FIG. 1 based on the estimation model, without conducting the physicalcrash test or the computer simulation.

FIG. 3 shows a block diagram of an apparatus 100, according to anembodiment of the present invention. The apparatus 100 may generate anestimation model and predict target characteristic data based on theestimation model. The apparatus 100 may comprise a processor and one ormore computer readable mediums collectively including instructions. Theinstructions, when executed by the processor, may cause the processor tooperate as a plurality of operation sections. Thereby, the apparatus 100may be regarded as comprising an obtaining section 110, a calculatingsection 130, a generating section 150, and a predicting section 170.

The obtaining section 110 may obtain a plurality of physical structuredata, such as the physical structure data of the car hood 12 shown inFIG. 1, and a plurality of characteristic data, such as theacceleration-time curve shown in FIG. 2, as a training data. Theobtaining section 110 may provide the generating section 150 with theplurality of physical structure data, and provide the calculatingsection 130 with the plurality of characteristic data, as the trainingdata.

The obtaining section 110 may also obtain a new target physicalstructure data, and provide the predicting section 170 with the newtarget physical structure data for prediction of the estimation model.

The calculating section 130 may calculate a characteristic similaritybetween a first characteristic data and a second characteristic dataamong the plurality of characteristic data. The calculating section 130may calculate one or more of the characteristic similarities among aplurality of pairs of characteristic data among the plurality of thecharacteristic data. The calculating section 130 may provide thegenerating section 150 with the calculated characteristic similarities.

The generating section 150 may generate an estimation model forestimating a target characteristic data from a target physical structuredata. The generating section 150 may comprise a first determiningsection 152 and a second determining section 154.

The first determining section 152 may conduct a first learning todetermine a similarity function that estimates a similarity between twophysical structures of the two physical structure data.

The first determining section 152 may also estimate at least onestructural similarity between at least two physical structures thatcorrespond with physical structure data among the plurality of physicalstructure data, based on the similarity function determined by the firstlearning. The first determining section 152 may provide the seconddetermining section 154 with one or more of the estimated structuralsimilarities.

The second determining section 154 may conduct a second learning togenerate the estimation model. The estimation model may include having aweight and sensitivity, and the second determining section 154 mayoptimize the weight and sensitivity in the second learning. The seconddetermining section 154 may provide the predicting section 170 with thegenerated estimation model.

The predicting section 170 may estimate a target characteristic data ofa new target physical structure by using the estimation model. In oneembodiment, the predicting section 170 may perform the estimation basedon the estimation model by using at least one structural similaritybetween the target physical structure data and each of the plurality ofthe physical structure data in the training data. In one embodiment, thepredicting section 170 may input the new target physical structure dataand estimate the characteristic data corresponding to the new targetphysical structure data, based on the estimation model.

As described above, the apparatus 100 can estimate characteristic databased on physical structure data by performing the first learning andthe second learning, without actually generating a computer simulationof the physical structure data, thereby reducing cost and time formanufacturing products, such as cars.

FIG. 4 shows an operational flow of an apparatus, according to anembodiment of the present invention. The present embodiment describes anexample in which an apparatus, such as the apparatus 100, performs theoperations from S410 to S470, as shown in FIG. 4. FIG. 4 shows oneexample of the operational flow of the apparatus 100 shown in FIG. 3,but the apparatus 100 shown in FIG. 3 is not limited to using thisoperational flow.

First, at S410, an obtaining section, such as the obtaining section 110,may obtain training data from a memory of the apparatus or a databaseoutside the apparatus. The training data may include data of a pluralityof points in a physical structure, such as a car hood. Data of eachpoint may include physical structure data and characteristic data.

The obtaining section may provide the generating section with theplurality of the physical structure data, and provide the calculatingsection with the plurality of the characteristic data.

Next, at S420, a calculating section, such as the calculating section130, may calculate a characteristic similarity between a firstcharacteristic data and a second characteristic data among the pluralityof characteristic data. The calculating section may generate allpossible pairs or some of the all possible pairs from the plurality ofcharacteristic data, and calculate the characteristic similarity of eachof the two characteristic data in each of the pairs. In one embodiment,the calculating section may calculate a characteristic similarity of thecharacteristic data Y₁ and Y₂ (which may be referred to as S(Y₁, Y₂)), acharacteristic similarity S(Y₁, Y₃), a characteristic similarity S(Y₁,Y₄), . . . , a characteristic similarity S(Y_(N−2), Y_(N)), acharacteristic similarity S(Y_(N−1), Y_(N)), wherein a variable Nrepresents a number of training data, such as a number of points of thecar hood(s).

The calculating section may perform the calculation based on at leastone difference between corresponding characteristic values of the firstcharacteristic data and the second characteristic data. In oneembodiment, the calculating section may calculate the characteristicsimilarity S(Y_(i), Y_(j)) by calculating the Euclidean distance betweenvectors Y_(i) and Y_(j). The calculating section may provide thegenerating section with the calculated characteristic similarities.

Next, at S430, a first determining section, such as the firstdetermining section 152, may conduct a first learning to determine thesimilarity function for estimating a new structural similarity of twophysical structures of the physical structure data.

Next, at S440, the first determining section may estimate structuralsimilarity of the physical structures corresponding physical structuredata among the plurality of physical structure data based on the learnedsimilarity function. Thereby the first determining section can performthe estimation based on at least one characteristic similarity betweencharacteristic data that corresponds with the at least two physicalstructures.

The first determining section may estimate the structural similaritiesof pairs of physical structure vectors (e.g., a pair (Xs₁, Xs₂), a pair(Xs₁, Xs₃), a pair (Xs₁, Xs₄), . . . , a pair (Xs_(N−2), Xs_(N)), a pair(Xs_(N−1), Xs_(N))) by calculating output values of the similarityfunction L₁(Xs₁, Xs₂), L₁(Xs₁, Xs₃), L₁(Xs₁, Xs₄), . . . , L₁(Xs_(N−2),Xs_(N)), L₁(Xs_(N−1), Xs_(N)). The first determining section may providethe second determining section with the estimated structuralsimilarities.

Next, at S450, the second determining section, such as the seconddetermining section 154, may conduct a second learning to determine aprediction function for estimating the characteristic data from thephysical structure. In the second learning, the second determiningsection 154 may optimize an objective function as shown in formula (1):

Arg min Σ_(t) ^(T)Σ_(i) ^(N) L ₂(y _(it)−φ(X _(i))^(T)ω)²+λ|ω|²  formula(1)

where φ(⋅) is a function that represents a type of Gaussian kernel, ω isa weight vector including a plurality of weight variables, λ is aregularization term (e.g., a L2 regularization term), y_(it) is a targetcharacteristic value of a target characteristic data Y_(i).

As explained above, the second determining section may determine theprediction function that outputs a plurality of values, such asacceleration values in the acceleration-time curve of an object thathits each point. In other words, the second determining section performsa multi-label liner regression. The second determining section mayprovide the predicting section with the estimation model.

Next, at S460, the obtaining section may obtain a new target physicalstructure data for predicting a target characteristic data. In oneembodiment, the obtaining section may obtain a new target physicalstructure data X_(i′) of a new target car hood. The obtaining sectionmay provide the predicting section with the new target physicalstructure data.

Next, at S470, the predicting section, such as the predicting section170, may estimate characteristic data of the new target physicalstructure′ by using the estimation model. In one embodiment, thepredicting section may estimate the characteristic data Y_(i′) of thenew target physical structure data X_(i′) by inputting X_(i′) and valuesof the time variable t into the estimation model φ(X_(i))^(T)ω. Theestimated characteristic data Y_(i′) may include characteristic values,each corresponding to an acceleration value with respect to the time tinan acceleration-time curve.

FIG. 5 shows one example of the physical structure data X_(i), accordingto an embodiment of the present invention. Each physical structure dataof the plurality of physical structure data obtained by an obtainingsection, such as the obtaining section 110, may include a featurerepresenting physical structure of a point of a physical structure, afeature representing location (e.g., absolute position or relativeposition) of the point of the physical structure, etc and time.

In the embodiment, of FIG. 5, the physical structure data X_(i) maycorrespond to the point P_(i) in the car hood of FIG. 1 and may berepresented by a vector including scalar values s_(i1), s_(i2), . . .,s_(iS), p_(i1), p_(i2), . . . , p_(iP), and t. The scalar valuess_(i1), s_(i2), . . . ,s_(iS) may represent a shape around the pointP_(i) in the car hood. In one embodiment, the scalar values s_(i1),s_(i2), . . . , s_(iS) may be values representing a relative height of Spoints (e.g., 10 points) in the car hood around the point P_(i). Thescalar values s_(i1), s_(i2), . . . , s_(iS) may form a vector Xs_(i).

The scalar values p_(i1), p_(i2), . . . , p_(iP) may be valuesrepresenting a location of the point P_(i). In one embodiment, a valueof P may be 3, and p_(i1), may correspond to a relative location in afirst dimension (x-axis) of the point P_(i), p_(i2) may correspond to arelative location in a second dimension (y-axis) of the point P_(i), andp_(i3) may correspond to a relative location in a third dimension(z-axis) of the point P_(i).

The scalar values p_(i1), p_(i2), p_(i3) and t may form a locationvector Xp_(i). In addition to or instead of Xs_(i) and/or Xp_(i), theobtaining section may obtain the physical structure data includingvalues representing other features of the point P_(i), such as thicknessof one or more of points in the car hood around the point P_(i). Thescalar value t may correspond to time. The plurality of time at whichacceleration values are obtained in the acceleration-time curve maycorrespond to a variable t in Xp_(i). In other words, the firstdetermining section may prepare a plurality of Xp_(i) having differentvalue of time t for each acceleration-time curve, and each value of timet corresponds to the time at which an acceleration value is obtained inthe acceleration-time curve.

FIG. 6 shows one example of the characteristic data Y_(i). Eachcharacteristic data of the plurality of characteristic data maycorrespond to a physical structure among the plurality of physicalstructures. Each characteristic data may include at least onecharacteristic value among the plurality of characteristic values thatrepresents a change of a characteristic relating to time. The change ofa characteristic relating to time may be a characteristic relating to animpact against the corresponding physical structure (e.g., anacceleration of an object hitting a point of physical structure), orrepresenting a transformation of the corresponding physical structure.

In one embodiment, the characteristic data Y_(i) corresponds to thephysical structure data X_(i). For example, each characteristic dataY_(i) may represent a vector including a plurality of characteristicvalues y_(i1), y_(i2), . . . , and y_(iT). Each characteristic valuey_(it) of the characteristic data Y_(i) may represent a characteristicof a physical structure that corresponds with a physical structure dataX_(i) among the plurality of physical structure data.

In one embodiment, the characteristic data Y_(i) may correspond toacceleration values in the acceleration-time curve of the point P_(i)having the physical structure data X_(i), as shown in FIG. 2. In theembodiment, each of characteristic values y_(i1), y_(i2), . . . , y_(iT)corresponds to a value of acceleration of the point Pi at each time t₁,t₂, . . . , t_(T).

FIG. 7 shows one example of the first learning. The first determiningsection may learn a similarity function L_({1,d})(⋅, ⋅). In oneembodiment, the similarity function L_({1,d})(Xs_(i), Xs_(j)) estimatesthe structural similarity between a vector Xs_(i) of the physicalstructure data X_(i) and a vector Xs_(j) of the physical structure dataX_(j). The first determining section may learn the similarity functionL_({1,d})(⋅, ⋅) such that output values of the similarity functionL_({1,d})(Xs_(i), Xs_(j)) match with the similarity of thecharacteristic data (Y_(a), Y_(b)), which corresponds to the physicalstructure data (Xs_(a), Xs_(b)).

For the first learning, the first determining section may first generatea plurality of pairs of vectors of Xs from the plurality of physicalstructure data in the training data that correspond to the pairs of thecharacteristic data generated at S420. In one embodiment, the firstdetermining section may generate a pair (Xs₁, Xs₂), a pair (Xs₁, Xs₃), apair (Xs₁, Xs₄), . . . , a pair (Xs_(N−2), Xs_(N)), a pair (Xs_(N−1),Xs_(N)).

Then, the first determining section may determine the similarityfunction for estimating a new structural similarity based on the atleast one structural similarity. In one embodiment, the firstdetermining section may determine the similarity function based on atleast one characteristic similarity between characteristic data thatcorresponds with the at least two physical structures. In theembodiment, the first determining section may determine D kinds of thesimilarity function L_({1,1})(Xs_(i), Xs_(j)), L_({1,2})(Xs_(i),Xs_(j)), . . . , L_({1,D})(Xs_(i), Xs_(j)) using formula (2) as shownbelow:

L _({1,d})(Xs _(i) ,Xs _(j))=Σ_(c) g _(c,d)(Xs _(i) ,Xs _(j))·f _(c,d)(Y_(train))  formula (2)

where c is a variable that represents perspective of the similarity,g_(c,d)(⋅, ⋅) is a function that generates a division rule of the treemodel based on input vectors, and f_(c,d)(Y_(train)) is a function thatbrings out two characteristic data, wherein those two characteristicdata correspond to Xs_(i) and Xs_(j) and are brought out in theviewpoint of the perspective of the similarity c.

In one embodiment, the function g_(c,d)(Xs_(i), Xs_(j)) evaluates thesimilarity of Xs_(i) and Xs_(j) in a perspective of similarityrepresented by a value of the variable c. Therefore, the functiong_(c,d)(Xs_(i), Xs_(j)) evaluates the similarity of Xs_(i) and Xs_(j) indifferent perspectives for each value of the variable c.

The function g_(c,d)(Xs_(i), Xs_(j)) may evaluate the similarity of asubset of variables among scaler variables in the vectors Xs_(i) andXs_(j). In one embodiment, the function g_(1,d)(Xs_(i), Xs_(j)) mayevaluate the similarity of the first 3 variables in the vectors Xs_(i)and Xs_(j) (i.e., s_(i1), s_(i2), s_(i3) in Xs_(i) and s_(j1), s_(j2),s_(j3) in Xs_(j)). The function g_(2,d)(Xs_(i), Xs_(j)) may evaluate thesimilarity of the next 3 variables in the vectors Xs_(i) and X_(sj)(i.e., s_(i4), s_(i5), s_(i6) in Xs_(i) and s_(j4), s_(j5), s_(j6) inXs_(j)). Thereby, the functions g_(1,d)(Xs_(i), Xs_(j)), g_(2,d)(Xs_(i),Xs_(j)), . . . , g_(C,d)(Xs_(i), Xs_(j)) evaluate the vectors Xs_(i),Xs_(j) in different C perspectives.

In the embodiment, if s_(i1), s_(i2), s_(i3) in Xs_(i) and s_(j1),s_(j2), s_(j3) in Xs_(j) are determined to be similar, then the functiong_(1,d)(Xs_(i), Xs_(j)) may output 1, and other functionsg_(2,d)(Xs_(i), Xs_(j)), g_(3,d)(Xs_(i), Xs_(j)), . . . ,g_(C,d)(Xs_(i), Xs_(j)) may output 0. And if s_(i4), s_(i5), s_(i6) inXs_(i) and s_(j4), s_(j5), s_(j6) in Xs_(j) are determined to be similarbased on the tree model, then the function g_(2,d)(Xs_(i), Xs_(j)) mayoutput 1, and other functions g_(1,d)(Xs_(i), Xs_(j)), g_(3,d)(Xs_(i),Xs_(j)), . . . , g_(C,d)(Xs_(i), Xs_(j)) may output 0.

The first determining section may determine functions g_(c,d)(⋅, ⋅) inthe first learning. The first determining section may determinefunctions g_(c,d)(⋅, ⋅) of the similarity function L₁(⋅, ⋅) by using anon-linear model (e.g., a tree model). The first determining section mayuse a neural network model instead of the tree model.

The function f_(c,d)(Y_(train)) firstly brings out, from a plurality ofcharacteristic data of input training data Y_(train) (e.g., allcharacteristic data Y₁, Y₂, . . . , Y_(N)), two characteristic data(e.g., Y_(a) and Y_(b)) of which two corresponding physical structuredata (e.g., X_(a) and X_(b)) are evaluated as being similar by thecorresponding function g_(c,d)(X_(a), X_(b)), and then evaluates thesimilarity of these two characteristic data Y_(a) and Y_(b).

FIG. 8 shows one example of φ(X₁). As shown in FIG. 8, φ(X_(i)) is afunction that inputs the physical structure data X_(i), and outputs avector having a plurality of values estimated based on X_(i). As shownin FIG. 8, the vector of φ(X_(i)) may include output values of thesimilarity functions K(⋅, ⋅) of an input vector V(X_(i), X_(n)) of thephysical structure data X_(i) and each of the physical structure dataX₁, . . . ,X_(N) in the training data. The first determining section mayprepare D kinds of g_(c,d)(⋅, ⋅) functions and f_(c,d)(Y_(train))functions and thereby determining D kinds of the similarity functions.

FIG. 9 shows one example of V(X_(i), X_(n)), according to an embodimentof the present invention. As shown in FIG. 9, V(X_(i), X_(n)) may inputX_(i), and X_(n), and output a vector including elements of outputvalues of functions θ₁×L_({1,1})(X_(i),X_(n)),θ₂×L_({1,2})(X_(i),X_(n)), . . . , θ_(D1)×L_({1,D1})(X_(i),X_(n)) andoutput values of functions θ_({D1+1})×L_({2,1})(X_(i),X_(n)),θ_({D1+2})×L_({2,2})(X_(i),X_(n)), . . . ,θ_({D1+D2})×L_({2,D2})(X_(i),X_(n)).

In one embodiment, the d1-th function L_({1,d1})(X_(i),X_(n)) in theV(X_(i), X_(n)) may correspond to L_({1,d1})(Xs_(i), Xs_(j)) of theformula (2).

The {D1+d2}-th function L_({2,d2})(X_(i),X_(n)) in the V(X_(i), X_(n))may be a mean squared error function or an absolute error function ofvalues of the location vectors Xp_(i) and Xp_(n), and may be representedas shown below:

L _({2,d2})(X _(i) ,X _(n))=(Xp _(i) −Xp _(n))²  formula (4).

Alternatively, function L_({2,d2})(X_(i),X_(n)) may be:

L _({2,d2})(X _(i) ,X _(n))=|Xp _(i) −Xp _(n)|  formula (5).

Other implementation of D2 kinds of functions L_({2,d2}) is alsopossible. The variables θ₁, θ₂, . . . , θ_(D1), θ_(D1+1), . . . ,θ_(D1+d2) represent sensitivity of each element in the vector V(X_(i),X_(n)). The generating section may also learn the D1+D2 variables θ. Asdescribed, the generating section provides D1 kinds of L_({1,d1})functions and D2 kinds of L_({2,d2}) functions.

The vector φ(X_(i)) may be represented by:

φ(X _(i))=θ₀ exp[−1/2×Σ_({j,k,d})(θ_(d) ×L _({j,k})(X _(i) ,X_(j)))]  formula (6).

The weight vector w includes weight variables that correspond to each ofthe functions K(V(X_(i), X_(n))). In one embodiment, the weightvariables w₁, w₂, . . . , w_(N) correspond to K(V(X_(i), X₁)),K(V(X_(i), X₂)), . . . , K(V(X_(i), X_(N))).

The second determining section may determine the weight ω andsensitivities θ of an estimated characteristic value to decrease adifference between the estimated characteristic value φ(X_(i))^(T)ω anda target characteristic value y_(it) of a target characteristic dataY_(i) by using the formula (1). The function L₂(⋅) in the objectivefunction in the formula (1) may be a loss function such as a meansquared error function or an absolute error function.

During the second learning, the second determining section may learnvalues of the weight vector ω and values of parameters θ₀, θ₁, . . .,θ_(D1), . . . , θ_({D1+D2}) in φ(X_(i)). The second determining sectionmay determine the weight vector ω and the values of parameters θ₀, θ₁, .. . , θ_({D1+D2}) in an alternative manner. The second determiningsection may determine the weight by using a kernel method (such as ARDkernel) and a ridge regression. In other embodiments, the weight vectormay include any number of weights.

The second determining section may use φ(X_(i))^(T)ω in the optimizedresult of the formula (1), as the estimation model. Since the φ(X_(i))represents a composition of a plurality of structural similarities ofthe target physical structure data with each physical structure data ofthe plurality of physical structure data and a composition of theplurality of relative positions between the target structure and eachphysical structure, the characteristic value may be estimated based onthese compositions by the estimation model.

As explained above relating to the operational flow of FIGS. 4-8, theapparatus can estimate a high level feature (such as theacceleration-time curve) from a low level feature (such as shape of thecar hood), by utilizing a hierarchical model (such as the first learningand the second learning). In one example, the apparatus can predict anacceleration curve of the head-hood impact of a new design of the carhood, without actually conducting the physical test or the crashsimulation.

FIG. 10 shows one example of a response surface of the acceleration-timecurve. One axis in the graph in FIG. 10 corresponds to the accelerationvalue, another axis in the graph in FIG. 10 corresponds to the time, andthe other axis in the graph in FIG. 10 corresponds to high levelparameters derived from the simulation based on the physical structuredata. The response surface may represent the estimation model generatedby the second determining section, and a curve derived from the responsesurface by slicing the response surface in a plane of the time axis andthe acceleration axis represents an acceleration-time curve.

The description in relation to FIGS. 1-10 mainly treats a car hood asthe physical structure and an acceleration-time curve as thecharacteristic data. However, other implementations are also possible.

FIG. 11 shows an exemplary configuration of a computer 1900 according toan embodiment of the invention. The computer 1900 according to thepresent embodiment includes a CPU 2000, a RAM 2020, a graphicscontroller 2075, and a display apparatus 2080, which are mutuallyconnected by a host controller 2082. The computer 1900 also includesinput/output units such as a communication interface 2030, a hard diskdrive 2040, and a DVD-ROM drive 2060, which are connected to the hostcontroller 2082 via an input/output controller 2084. The computer alsoincludes legacy input/output units such as a ROM 2010 and a keyboard2050, which are connected to the input/output controller 2084 through aninput/output chip 2070.

The host controller 2082 connects the RAM 2020 with the CPU 2000 and thegraphics controller 2075, which access the RAM 2020 at a high transferrate. The CPU 2000 operates according to programs stored in the ROM 2010and the RAM 2020, thereby controlling each unit. The graphics controller2075 obtains image data generated by the CPU 2000 on a frame buffer orthe like provided in the RAM 2020, and causes the image data to bedisplayed on the display apparatus 2080. Alternatively, the graphicscontroller 2075 may contain therein a frame buffer or the like forstoring image data generated by the CPU 2000.

The input/output controller 2084 connects the host controller 2082 withthe communication interface 2030, the hard disk drive 2040, and theDVD-ROM drive 2060, which are relatively high-speed input/output units.The communication interface 2030 communicates with other electronicdevices via a network. The hard disk drive 2040 stores programs and dataused by the CPU 2000 within the computer 1900. The DVD-ROM drive 2060reads the programs or the data from the DVD-ROM 2095, and provides thehard disk drive 2040 with the programs or the data via the RAM 2020.

The ROM 2010 and the keyboard 2050 and the input/output chip 2070, whichare relatively low-speed input/output units, are connected to theinput/output controller 2084. The ROM 2010 stores therein a boot programor the like executed by the computer 1900 at the time of activation, aprogram depending on the hardware of the computer 1900. The keyboard2050 inputs text data or commands from a user, and may provide the harddisk drive 2040 with the text data or the commands via the RAM 2020. Theinput/output chip 2070 connects a keyboard 2050 to an input/outputcontroller 2084, and may connect various input/output units via aparallel port, a serial port, a keyboard port, a mouse port, and thelike to the input/output controller 2084.

A program to be stored on the hard disk drive 2040 via the RAM 2020 isprovided by a recording medium as the DVD-ROM 2095, and an IC card. Theprogram is read from the recording medium, installed into the hard diskdrive 2040 within the computer 1900 via the RAM 2020, and executed inthe CPU 2000.

A program that is installed in the computer 1900 may cause the computer1900 to function as an apparatus, such as the apparatus 100 of FIG. 3.The program or module acts on the CPU 2000, to cause the computer 1900to function as a section, component, element such as each element of theapparatus 100 of FIG. 3 (e.g., the obtaining section 110, thecalculating section 130, the generating section 150, the predictingsection 170, and the like).

The information processing described in these programs is read into thecomputer 1900 such as the apparatus 100 of FIG. 3, to function as theobtaining section, which is the result of cooperation between theprogram or module and the above-mentioned various types of hardwareresources. Moreover, the apparatus is constituted by realizing theoperation or processing of information in accordance with the usage ofthe computer 1900.

For example, in response to communication between the computer 1900 andan external device, the CPU 2000 may execute a communication programloaded onto the RAM 2020, to instruct communication processing to acommunication interface 2030, based on the processing described in thecommunication program.

The communication interface 2030, under control of the CPU 2000, readsthe transmission data stored on the transmission buffering regionprovided in the recording medium, such as a RAM 2020, a hard disk drive2040, or a DVD-ROM 2095, and transmits the read transmission data to anetwork, or writes reception data received from a network to a receptionbuffering region or the like provided on the recording medium. In thisway, the communication interface 2030 may exchangetransmission/reception data with the recording medium by a DMA (directmemory access) method, or by a configuration that the CPU 2000 reads thedata from the recording medium or the communication interface 2030 of atransfer destination, to write the data into the communication interface2030 or the recording medium of the transfer destination, so as totransfer the transmission/reception data.

In addition, the CPU 2000 may cause all or a necessary portion of thefile of the database to be read into the RAM 2020 such as by DMAtransfer, the file or the database having been stored in an externalrecording medium such as the hard disk drive 2040, the DVD-ROM drive2060(DVD-ROM 2095) to perform various types of processing onto the dataon the RAM 2020. The CPU 2000 may then write back the processed data tothe external recording medium by means of a DMA transfer method or thelike. In such processing, the RAM 2020 can be considered to temporarilystore the contents of the external recording medium, and so the RAM2020, the external recording apparatus, and the like are collectivelyreferred to as a memory, a storage section, a recording medium, acomputer readable medium, etc.

Various types of information, such as various types of programs, data,tables, and databases, may be stored in the recording apparatus, toundergo information processing. Note that the CPU 2000 may also use apart of the RAM 2020 to perform reading/writing thereto on the cachememory. In such an embodiment, the cache is considered to be containedin the RAM 2020, the memory, and/or the recording medium unless notedotherwise, since the cache memory performs part of the function of theRAM 2020.

The CPU 2000 may perform various types of processing, onto the data readfrom a memory such as the RAM 2020, which includes various types ofoperations, processing of information, condition judging, search/replaceof information, etc., as described in the present embodiment anddesignated by an instruction sequence of programs, and writes the resultback to the memory such as the RAM 2020. For example, if performingcondition judging, then the CPU 2000 may judge whether each type ofvariable shown in the present embodiment is larger, smaller, no smallerthan, no greater than, or equal to the other variable or constant, andif the condition judging results in the affirmative (or in thenegative), then the process branches to a different instructionsequence, or calls a sub routine.

In addition, the CPU 2000 may search for information in a file, adatabase, etc., in the recording medium. For example, if a plurality ofentries, each having an attribute value of a first attribute isassociated with an attribute value of a second attribute, are stored ina recording apparatus, then the CPU 2000 may search for an entrymatching the condition whose attribute value of the first attribute isdesignated, from among the plurality of entries stored in the recordingmedium, and reads the attribute value of the second attribute stored inthe entry, thereby obtaining the attribute value of the second attributeassociated with the first attribute satisfying the predeterminedcondition.

The above-explained program or module may be stored in an externalrecording medium. Exemplary recording mediums include a DVD-ROM 2095, aswell as an optical recording medium such as a Blu-ray Disk or a CD, amagneto-optic recording medium such as a MO, a tape medium, and asemiconductor memory such as an IC card. In addition, a recording mediumsuch as a hard disk or a RAM provided in a server system connected to adedicated communication network or the Internet can be used as arecording medium, thereby providing the program to the computer 1900 viathe network.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium, which may implement thestorage section, may be, for example, but is not limited to, anelectronic storage device, a magnetic storage device, an optical storagedevice, an electromagnetic storage device, a semiconductor storagedevice, or any suitable combination of the foregoing.

A non-exhaustive list of more specific examples of the computer readablestorage medium includes the following: a portable computer diskette, ahard disk, a random access memory (RAM), a read-only memory (ROM), anerasable programmable read-only memory (EPROM or Flash memory), a staticrandom access memory (SRAM), a portable compact disc read-only memory(CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk,a mechanically encoded device such as punch-cards or raised structuresin a groove having instructions recorded thereon, and any suitablecombination of the foregoing. A computer readable storage medium, asused herein, is not to be construed as being transitory signals per se,such as radio waves or other freely propagating electromagnetic waves,electromagnetic waves propagating through a waveguide or othertransmission media (e.g., light pulses passing through a fiber-opticcable), or electrical signals transmitted through a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers, and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server.

In the latter scenario, the remote computer may be connected to theuser's computer through any type of network, including a local areanetwork (LAN) or a wide area network (WAN), or the connection may bemade to an external computer (for example, through the Internet using anInternet Service Provider). In some embodiments, electronic circuitryincluding, for example, programmable logic circuitry, field-programmablegate arrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

These computer readable program instructions may also be stored in acomputer readable storage medium that can direct a computer, aprogrammable data processing apparatus, and/or other devices to functionin a particular manner, such that the computer readable storage mediumhaving instructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s).

In some alternative implementations, the functions noted in the blockmay occur out of the order noted in the figures. For example, two blocksshown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts or carry outcombinations of special purpose hardware and computer instructions.

While the embodiment(s) of the present invention has (have) beendescribed, the technical scope of the invention is not limited to theabove described embodiment(s). It is apparent to persons skilled in theart that various alterations and improvements can be added to theabove-described embodiment(s). It is also apparent from the scope of theclaims that the embodiments added with such alterations or improvementscan be included in the technical scope of the invention.

The operations, procedures, steps, and stages of each process performedby an apparatus, system, program, and method shown in the claims,embodiments, or diagrams can be performed in any order as long as theorder is not indicated by “prior to,” “before,” or the like and as longas the output from a previous process is not used in a later process.Even if the process flow is described using phrases such as “first” or“next” in the claims, embodiments, or diagrams, it does not necessarilymean that the process must be performed in this order.

As made clear from the above, the embodiments of the present inventioncan be used to realize an apparatus, a method, and a computer programproduct for predicting a target characteristic data.

What is claimed is:
 1. An apparatus comprising: a processor configuredto: model physical structures from physical structure data correspondingto discrete points distributed across each of a plurality of physicalstructures; and estimate target characteristic data from a targetphysical structure data of a target physical structure by applying anobjective function to characteristic data of at least one of thephysical structures having a structural similarity to the targetstructure, the objective function identifying argument minimums for aloss function of a Gaussian weighted structural similarity multiplied bya weight vector.
 2. The apparatus of claim 1, wherein the structuralsimilarity is based on at least one characteristic similarity betweencharacteristic data that corresponds with at least two of the physicalstructures.
 3. The apparatus of claim 2, wherein the instructionsfurther cause the processor to calculate a characteristic similaritybetween a first characteristic data and a second characteristic dataamong the characteristic data, wherein the calculation is based on atleast one difference between corresponding characteristic values of thefirst characteristic data and the second characteristic data.
 4. Theapparatus of claim 1, wherein each characteristic data includes at leastone characteristic value among the plurality of characteristic valuesthat represents a change of characteristic with respect to time.
 5. Theapparatus of claim 4, wherein each characteristic data includes at leastone characteristic value among the plurality of characteristic valuesthat represents a change of a characteristic relating to an impactagainst the corresponding physical structure, or a transformation of thecorresponding physical structure.
 6. The apparatus of claim 5, whereineach physical structure is a part of a body of a mobile object.
 7. Theapparatus of claim 4, wherein each physical structure data includes afeature for representing a location of the physical structure and time.8. The apparatus of claim 1, wherein the instructions further cause theprocessor to determine a similarity function for estimating a newstructural similarity, the determination based on at least onecharacteristic similarity between characteristic data that correspondswith the at least two physical structures.
 9. The apparatus of claim 1,wherein the instructions further cause the processor to determine thestructural similarity by using a tree model or a neural network model.10. The apparatus of claim 1, wherein the instructions further cause theprocessor to determine a weight of an estimated characteristic value todecrease a difference between the estimated characteristic value and atarget characteristic value of a target characteristic data, wherein theestimated characteristic value is based on a composition of a pluralityof structural similarities of the target physical structure data witheach physical structure data.
 11. The apparatus of claim 10, wherein theinstructions further cause the processor to determine a sensitivity ofthe plurality of structural similarities of the target physicalstructure data with each physical structure data to decrease adifference between the estimated characteristic value and the targetcharacteristic value of the target characteristic data, wherein theestimated characteristic value is further based on a composition of theplurality of relative positions between the target structure and eachphysical structure.
 12. The apparatus of claim 11, wherein theinstructions further cause the processor to determine the weight byusing a kernel method.
 13. The apparatus of claim 1, wherein theinstructions further cause the processor to estimate characteristic dataof a target physical structure by using the estimation model.
 14. Acomputer-implemented method comprising: modeling physical structuresfrom physical structure data corresponding to discrete pointsdistributed across each of a plurality of physical structures; andestimating target characteristic data from target physical structuredata of a target physical structure by applying an objective function tocharacteristic data of at least one of the physical structures having astructural similarity to the target structure, the objective functionidentifying argument minimums for a loss function of a Gaussian weightedstructural similarity multiplied by a weight vector.
 15. Thecomputer-implemented method of claim 14, further comprising calculatinga characteristic similarity between a first characteristic data and asecond characteristic data among the characteristic data, wherein thecalculation is based on at least one difference between correspondingcharacteristic values of the first characteristic data and the secondcharacteristic data.
 16. The computer-implemented method of claim 14,wherein each characteristic data includes at least one characteristicvalue among the plurality of characteristic values that represents achange of a characteristic with respect to time.
 17. Thecomputer-implemented method of claim 16, wherein each characteristicdata includes at least one characteristic value among the plurality ofcharacteristic values that represents a change of a characteristicrelating to an impact against the corresponding physical structure, or atransformation of the corresponding physical structure.
 18. A computerprogram product comprising one or more non-transitory computer readablemediums collectively including instructions that, when executed by theprocessor, cause the processor to: model physical structures fromphysical structure data corresponding to discrete points distributedacross each of a plurality of physical structures; and estimate targetcharacteristic data from target physical structure data of a targetphysical structure by applying an objective function to characteristicdata of at least one of the physical structures having a structuralsimilarity to the target structure.
 19. The computer program product ofclaim 18, wherein the instructions further cause the processor tocalculate a characteristic similarity between a first characteristicdata and a second characteristic data among the characteristic data, thecalculation based on at least one difference between correspondingcharacteristic values of the first characteristic data and the secondcharacteristic data.
 20. The computer program product of claim 18,wherein each characteristic data includes at least one characteristicvalue among the plurality of characteristic values that represents achange of a characteristic with respect to time.